Amazon aspires to be “Earth’s most customer-centric company.” Numerous mission statements are sprinkled with customer focus. But in reality it is hard to actually be customer centric. Many companies talk about it but only a few achieve the holy grail!
I loved the concept that Prof Ranajoy Gulati speaks about: “In fact the big leap that companies need to make is to “not sell what they produce” but to “solve customer problems” & then suddenly “who produces the product is no longer important “because you start “owning the problem space”.
Customer-centric companies tracked by Gulati between 2001 and 2007 delivered shareholder returns of 150 percent while the S&P 500 delivered 14 percent. While research like this looks great, one wonders why there are not too many companies who “really put the customer at the centre”. Only companies who are great at “disruption” seem to truly belong to the category of obsessively customer focused. A customer-centric organization’s business is built completely around the customer. This kind of company has a strong understanding of the customer’s value and what the customer represents to the business’s profitability. With this knowledge, a customer-centric company adapts everything it does – from R&D to Customer service – to deliver the best value at the right cost to their customer.
The old adage that customers are always correct may not always be right. Current breed of customers are spoilt for choice & empowered. E commerce industry in India is a classic example where consumers are getting pampered with discounts. But there is a difference between “customer friendliness” & “customer centricity”. Companies create value for customers but also have to capture value in their business to be sustainable. So are companies like Amazon & Zappos the outliers? Zappos actually says that If a customer calls for a product and Zappos does not have the product in stock, they recommend a competitor who has it. While Zappos will lose the sale, in the long run it’s best for Zappos because the customer appreciates the help and tells their friends the story. It creates word of mouth.
Peter Fader, of HBS, has this interesting take: “Customer centricity is a strategy to fundamentally align a company’s products and services with the wants and needs of its most valuable customers. That strategy has a specific aim: more profits for the long term. This is a goal that every business would like to achieve… But you’ll only be able to get there and put customer centricity to use if you are willing to start thinking in new—and in some cases, truly radical—ways.”
So how should companies become more Customer centric:
Sort customers into –who gives value & who does not?
Operations-develop the ability to deliver different products & services to different customers-not easy! At first the operations folk will always say no!
Creating a customer-centric culture where you don’t script every interaction. Therefore, employees need to be able to make the right judgment calls on their own when dealing with customers.
Focus on customer satisfaction & retention over customer acquisition. In a 2013 Forrester survey of global CMOs, 63% listed acquiring new customers as their top priority, while just 22% said retaining current customers was their top goal. In India this is particularly hard because of low penetration of products & services.
Customer-obsessed enterprises must migrate investment budgets from areas that traditionally created dominance — brand advertising, distribution, mergers for scale, and supplier relationships — and invest in Retention & customer experiences
Prof Niraj Dewar, Professor in marketing at the Ivey Business School has this interesting comment: “Companies’ upstream activities, such as sourcing, production, and logistics are being commoditized or outsourced, while downstream activities aimed at shaping customers’ perception and reducing their costs and risks are emerging as the main sources of competitive advantage. To compete effectively, companies must shift their focus from upstream to downstream activities, emphasizing how they define their competitive set, influence customers’ purchase criteria, innovate to solve customer problems, and build advantage by accumulating customer data and harnessing network effect”.
I had written about this earlier as well. What does Customer centricity mean to you? I would love to have your feedback. Here is what I feel:
Being loyal to customers & not the other way around (customers needing to be loyal to the company). This needs companies to have a longer term view of customer lifetime value & not a short term view of immediate profit. It needs an internal senior level stakeholder who champions the customer cause (CMO?)
Become more accessible to customers & respond faster to their needs. This needs companies to move from “insight to action”. To act faster, companies need to break silos within their organisation to be able to respond to customers.
Use information to make every interaction relevant & use customer data to more powerfully personalise company’s interactions with the customer. The Big data world is only producing more such information for marketers to leverage. This amounts to a mass customisation strategy where the CIO & CMO need to work very closely together to make meaningful changes in the company’s operating environment. And most critically, to do this keeping the customers sensitivity to privacy as paramount!
Strategically think through what culture changes the enterprise needs to become more customer centric. Today technology & Big data based insights can help you accelerate this process.
For those of you in Mumbai, we at Cequity are doing this wonderful conference called Customer centricity world 2015
which has a keynote by Dr Bala Balachandran.
Do join us for this conference & you can learn more here: http://www.customercentricityworld.com/#about
The chief marketing officer and the chief information officer have become the corporate board room’s odd couple.
According to Wall street Journal: “As marketing budgets shift to relatively newer channels like social media marketing and mobile advertising via complex advertising technology vendors, marketing executives have in recent years been tasked more and more with understanding technology — while technology executives have been pushed to understand marketing”.
But are Marketers really investing a lot in Technology. Two years back Gartner predicted that by 2017 CMO's will spend more on IT than CIO's. Is this happening? So how big is the market for marketing software today?
IDC has an answer to that question, $20.2 billion in 2014. IDC expects that the market will have a compound annual growth rate (CAGR) of 12.4% for the next five years, resulting in a $32.4 billion market by 2018.
IDC breaks that market down into four broad categories:
Interaction Systems — the majority of customer-facing marketing software advertising, digital commerce, marketing automation, web experience management, mobile apps, social media tools, etc.
Content Production and Management — internal authoring and publishing tools, CMS platforms, DAM platforms, etc.
Data and Analytics — storing data and producing insights from it, such as business intelligence, predictive analytics, financial analysis, and broader marketing analytics.
Management and Administration — internal communications tools, workflows, budgeting, expense tracking, MRM, project management, collaboration tools, etc.
And yet Marketers are finding it difficult to adopt technology. Success rates are not so high. Vendor hype is far higher than on the ground reality. Research shows that CMO's need to be better prepared to overcome some of the challenges & one key impact item can be a stronger Technology organisation supporting the Marketing technology implementations.
Forrester polled 308 marketing and tech leaders, finding that 44% of marketers believe the CIO hires staff with marketing experience, an improvement compared with the 19% figure from last year’s survey. But on the other side, 58% of tech leaders think that marketers actually understand marketing technology, compared with 71% of marketers who believe so, a gap indicating how marketers’ “self-confidence is inflated,” according to Forrester.
And further research showed the CMO involvement in Technology still lags
So what should CMO's do differently in 2015 that will prepare them for this Tech invasion of Marketing.
Action Items for CMOs
Marketing strategy should drive Tech purchase:
think through what elements of your marketing strategy you are trying to impact: Are you trying to scale up 1:1 marketing based on analytics ?Or is it critical to create a solid data infrastructure for all the disparate data that Marketing has access to? First crystallise the strategy.
Carefully consider what technology you want to buy:
Remember there are 947 companies at last count selling Marketing tech to you. Don’t allow a bundled sale where someone selling an Enterprise stack just bundles in some marketing software for a very low cost. Think about the implications about a wrong choice.
Look at building your Marketing tech operation with one primary Marketing technology provider as a hub & a few secondary best of breed point solutions as a spoke.
This will allow you to get a maximum level of baseline capability from one vendor & so reduce the complexity of managing multiple technology partners.
Think about what changes in the structure of your marketing team are you & your company ready to make:
Are you ready to hire a Chief marketing technology officer & will he report to the CIO or the CMO? Think collaboration rather than team expansion. The IT team can be your best friend if you get the structure right. Marketers who can truly understand the intersection of marketing and technology are rare. Most marketing organizations still struggle to find qualified people to support the evaluation, purchase, implementation and use of these new marketing technologies
Be more Process centric:
CMO’s are buying a lot of technology. The intent is that it will help make us better, smarter and more efficient marketers, but with every license comes a new login and new processes that must be implemented to encompass it in our day-to-day workflows. Technology loses its value if you don’t adapt your processes to take advantage of what the software brings to the table
Think ROI & partner the CFO from the start:
manage expectations about how quickly magic will happen, because it won’t. Process change & skill adoption takes time. Account for it. Don’t get surprised.
The Growth Hacker:
The growth hacker is someone or a small team of people who understands technology and probably even have some coding skills. They understand their organization’s digital landscape to discover potential opportunities or loopholes. So this unit creates a Big data plan & has the all round skills to quickly create pilots & show impact. Sometimes external partners with such strengths can become your Growth hacker partners
What does design have to do with finance, money and banking?
New banks in India ,IDFC & Bandhan, better be listening. A bank acquiring a creative organization- that’s news! Adaptive Path, a design and user experience consultancy has been acquired by Capitol One. And just before that Daniel Makoski, founder of Google’s modular Project Ara phone project joined Capital One.So how come banks are attracting this serious Creative talent!
In the new digital world, banking & creativity may not be oxymorons!
New banks in India have a unique opportunity to embed “digital” in the fabric of how they do business. But banks are complex with structures that don’t allow for speed. In many cases, eBusiness teams own the mobile banking strategy, but few eBusiness teams have an exclusive mandate over their firm’s mobile banking initiatives. This division of responsibility creates silos and adds significant complexity to the coordination and optimization of Digital efforts.And yet, the user experience is the key for more consumers to adopt the bank’s digital channels.
AdaptivePath CEO Brandon Schauer puts it well, when he writes:
"Whether we talk about greeting cards, mobile apps, or vacation get-aways, the experience is the product. From the perspective of customers, everything that goes into making up that experience—technology, materials, service support, or a supply chain—simply becomes the magic behind the experience.
Yet the orientation and focus of our businesses is the inverse of this customer perspective. We plan around features and operational functions, leaving the customer experience as an unintentional byproduct of how the pieces and parts happen to come together for the customer".
As the infrastructure of digital technology — the chips, network connections, computing — becomes ever cheaper, they’re becoming commodities, and the value of tech products is shifting to the design and the user experience. But the real value starts to flow when companies orchestrate the User experience with Personalisation.
Personalization, it seems, is really about gathering exactly the data that’s needed in order to perform a particular task. Think about how Amazon asks users whether purchases were for themselves or as gifts, or how streaming services like Netflix and Pandora ask users to rate content. But personalization is a complex process involving multiple components:
The Financial services business actually can generate significant amount of user data to help personalize its offerings. Here is an interesting example of a Lending company in California, LendUp, which is doing this effectively.
LendUp wants to give those looking for a speedy fix to a short-term financial need, a way to borrow money without hidden fees and high interest rates. LendUp believes that really understanding its users makes all the difference in the world. The company is trying to be a low-friction source of relatively cheap loans for under banked individuals.
LendUp’s solution is pairing smart site design with smarter algorithms.
LendUp asks for standard data from each applicant (including Social Security number so it can look at credit scores and other data), but also asks certain applicants to connect using Twitter and Facebook. Also LendUp offers financial literacy education in the form of online webinars. Customers are incentivized to use these facilities with the knowledge that participation improves their loan rates and potentially increases their loan size in the future. LendUp found that borrowers who completed at least one of their free education modules were 80% more likely to repay. LendUp also found that repayment rates continue to rise as borrowers complete more education modules, suggesting that borrowers can apply and build on knowledge gained through a bank sponsored education program.
Obviously, the data LendUp generates about how people interact (by completing those credit-building lessons, for example) and repay once they’re in the system also helps the company determine future rates.
So quite a few pointers for us as Marketers:
- This is the age where Creative & data intersect. If banks have realised it, many other industries can profit from it.
- Thinking about the user interface for consumers, making it intuitive & easy & allowing data to be picked up through gamification are possible ways ahead.
- Analytics can help in continously fine tuning the User interface & improving the consumer experience.
If you thought Uber was just a car service company, it's a tech company that happens to be a car service too. No wonder it has neuroscientists amongst more traditional hires in the company! The data team at Uber uses data science for fundamental problems such as ETA algorithms (“Your driver will be here in 5 minutes”), pricing algorithms, fare estimators, and heat maps to show passengers the current position of their driver.
Uber‘s $1.2 billion financing tells a story- the imputed value for Uber (pre-money, i.e., prior to the influx of $1.2 billion) was $17 billion, a mind-boggling sum for a business that generates a couple of hundred million in revenues.
Both Lyft (another car sharing company) & Uber have attracted massive financing. Now that each team has a quarter-billion dollars in its pocket, the World championships can begin.
Uber is trying to use the "movement pattern" data that it gets to more sharply understand its users. Here are 3 examples in the words of the Uber data scientists that I found fascinating:
- Understanding Destination choices: Where has this person gone in the past? Do they frequent a certain bar? Where do other Uber users go? What businesses are popular generally? These are the basic questions the algorithm asks. On top of that, it smartly considers factors like time of day (people don't typically go to night clubs at 11 a.m.), distance (people aren't likely to get dropped off too far from their actual destination) and even the Zip code of each destination (Sketchy neighborhood? They probably didn't want to walk far, so the destination is likely near the drop-off point).
- Static or dynamic drivers:In another blog post , Uber data scientist Bradley Voytek explains how Uber’s “science team” simulated a city and learned that taxi drivers can just stay parked between trips and make twice as much as those who drive around in search of passengers. Uber discovered that “drivers who are constantly, randomly moving around a simulated city travel 10-20 times the distance compared to drivers who remain stationary or gravitate back toward a demand density between trips,” Voytek writes.
- Prostitution, Alcohol & Uber: To examine this Uber data scientists’ looked at the correlation between the number of each type of crime and the number of trips they've done in each neighbourhood. All types of crime except murder, vehicle theft, and arson were positively correlated with number of trips. After correcting for multiple comparisons, four crimes remained significantly correlated: Prostitution,Alcohol,Theft,Burglary. In other words: The parts of San Francisco that have the most prostitution, alcohol, theft, and burglary also have the most Uber rides! Party hard but be safe, Uberites!
So once you start to see the company as a data, tech & analytics company, the possibilities really start to become huge:
- Uber is now going after a huge new opportunity by changing the way business users can book and expense rides on its platform. The company is launching a new offering, called “Uber for Business”, which is designed to make it easier for users to bill trips directly to their company while working. Uber is providing participating companies with a centralized dashboard which they can use to keep track of rides that have been expensed. The new product is basically an acknowledgment that many consumers have been using Uber for both personal and business use cases, but their employers didn’t have a good way to manage those expenses.
- Uber has also moved into the fast-food delivery industry with its new service "UberFRESH", which it claims will deliver meals from local restaurants in less than 10 minutes. Uber isn’t taking on a fleet of new driving staff for the service. Instead it’s going to use its taxi drivers to relay the food between restaurant and customer. There will also be no extra delivery cost but drivers won’t leave their car to hand over the food, customers will have to collect it from the street.
How relevant is all this to a company in any traditional business? Here are some thoughts:
- It’s never too late to start embedding data based thinking in your business. Data based companies like Google, AirBnB, Uber etc will get to your industry at some point.
- Think about cross functional IT & business teams that are programmed like a guerrilla unit to solve specific problems.Get creative people into these roles.
- Introspect on what kind of people you are attracting for your analytics team-look for non traditional hires from the science field!
- Ask yourself whether you are keeping all your data. Storage is less expensive now. At Uber, they have got every GPS point for every trip ever taken at Uber, going back to the Trip #1
- One of the delights of using Uber is getting your receipt by email once your ride is done-for many businesses this could be a simple way of creating a customer experience & continuing to build a customer database.
As city-wide urban infrastructures such as buses, taxis, public utilities and roads become digital, the datasets obtained can be used for tracking movement patterns through space and time.
The identification, analysis and comparison of such patterns will provide greater insights on human movement and contribute to a better urban management and would be useful information for urban transport services provider. Imagine if Uber & a bunch of other companies started to share such data with other companies. There could be huge power in “community analytics”. If Coke & Uber were to cooperate by mashing up their data together, interesting opportunities could develop from such partnerships. Where people travel & when they consume can have interesting parallels.
How can Lingerie & data have any correlation? I am sure you are asking that questions. But bear with me & don’t forget to watch the video that I have provided a link to.
But before that, allow me to digress a bit. Almost 78% of consumers think it is hard to trust companies when it comes to use of their personal data (Orange, The Future of Digital Trust, 2014). And yet Personal data has become a currency today. All of us are leaving our data behind in a digital exhaust that has begun to worry us as consumers.
So, the World Economic Forum is calling personal data a ‘new asset class’: “a valuable resource for the 21st century that will touch all aspects of society”. But companies will need to understand how they can gather customer information without compromising the customer’s trust!
A recent PEW report had this to say:
“While enthusiasts see great potential for using Big Data, privacy advocates are worried as more and more data is collected about people - both as they knowingly disclose things in such things as their postings through social media and as they unknowingly share digital details about themselves as they march through life. Not only do the advocates worry about profiling, they also worry that those who crunch Big Data with algorithms might draw the wrong conclusions about who someone is, how she might behave in the future, and how to apply the correlations that will emerge in the data analysis.”
But some companies are finding a way where consumers share information because they get "value" in return.
True & co is this interesting company that combines data & design to create an opportunity for consumers to share data with the company thereby improving the appropriateness of the product to the customer. True & co claims to be the first company to fit women into their favourite bra with a fit quiz – no fitting rooms, no measuring tape, no photos. The data they collect allows them to match the customer to over 6000 body types on their database.
Research suggests that women loathe the bra shopping experience and the massive $14B intimate apparel industry is dominated by one primarily brick-and-mortar player. So True & co uses big data to make shopping online for lingerie easier & better. They collect over Half a million data points from users to help customise the experience. Since the company launched in 2012, True & Co has collected some 7 million data points They used this data to launch products designed using this data. Body type, implicit explicit preferences etc all mashed together to create a personalised recommendation engine.
Do have a look at this video telling their story:
So consumers are happy to share personal information as long as they see a “value add” for themselves. And organisations with trust-based information sharing relationships with customers will have significant competitive advantage over those with traditional data gathering relationships.
I was speaking at IIM Lucknow last night & had a wonderful session with the MBA students about the changing nature of Marketing & how Big data & Analytics are going to play a big part in it.
Prof Ashwani Kumar had a very interesting take on how Analytics is going to be eventually embedded across every function & not exist as just a separate specialisation.
Prof Ashwani is doing some interesting work on Analytics at IIM Lucknow & you can have a look at his work & profile here: http://linkd.in/XRQd
I spoke about how the changing nature of consumer behaviour is creating peta bytes of data for Marketers to analyse.And though Analytics is sexy today, companies still stuggle to adopt it & gain maximum mileage from it. So Analytics is popular but hard to execute! I also spoke about the need to bring creativity into analytics through both better "story telling" & more innovative approaches to data.
And yet in the words of a Gartner Analyst Doug Laney “companies have better sense of the value of office furniture than their information assets".
I also spoke about how the stock market seems to value “Information based companies” far more than any others.But most companies don't report Customer data: imagine if we had Customer flow & Customer value data along with the regular balance sheet & cash flow statements!!
Here is an interesting chart from Gartner which highlights the improved return on Information assets:
Here is a copy of my presentation:
Doug Laney has this very interesting view
“At Gartner, our infonomics research shows how information meets the criteria of a recognizable (balance sheet) asset. Yet, because the accounting aristocracy continues to prevent organizations from recognizing it, information continues to be managed with far less discipline than financial, physical assets or recognizable intangibles. We have also shown how organizations that are more information-centric have market-to-book values that are 200-300% higher than the S&P average”
Marketing is also changing a lot because of the access to huge amounts of Social media based customer data.
Not just marketing, Big data is hugely changing our world & life in fundamental ways. To see the enormity of this change, have a look at the video below...
I have seen innumerable situations where bright analysts are unable to “tell stories from their data”. They have a lot of learning to do from an unrelated field-Journalism!
Ben Fry has described it very well. Analytics or Data scientists need skills from these varied fields.
- Computer Science - acquire and parse data
- Mathematics, Statistics, & Data Mining - filter and mine
- Graphic Design - represent and refine
- Infovis and Human-Computer Interaction (HCI) - interaction
I believe that Analytics teams have a lot to learn from the new breed of Data journalists. They have all the above skills & also work with super deadlines!
At Cequity, our model is unique because it tries to integrate very contrasting dimensions into one entity where the sum is larger than the parts! Having a designer’s sense with data may contrast with a statistician’s dry look at numbers!
We seek “intersection” skills-intersection of Creative, technology, data & business! Not easy to do with highly talented people & we are attempting it!
The interesting thing is that journalism is getting far savvier with data! I see visual data based story telling in the New York Times that is absolutely mind boggling. Even here in India, I see some lovely data visualization in the Mint!
But are analysts getting creative with their story telling? Visualization of data is getting democratized & it is not very difficult for analysts to be creative about this. Today we as consumers are getting far savvier about technology in our personal lives & that will impact our expectations at the work place. I am sure that savvy consumers will make data presentation so much more fun even within “enterprises”.
I also wrote on this theme earlier here
In the Media world,new business models are emerging in which data is a raw material for profit, impact, and insight, co-created with an audience that was formerly a passive consumer!
In 2014, data journalism is mainstream and the market for data journalists is booming.New media outlets like FiveThirtyEight.com and Vox.com are competing for eyeballs with Appp3d.com from the Mirror, QZ.com from the Atlantic Media Group &The Economist’s DataBlog.
The New York Times hired biologist and machine models expert Chris Wiggins, an associate professor of applied mathematics at Columbia University, as its chief data scientist.
“At The New York Times, we produce a lot of content every day, but we also have a lot of data about the way people engage with that content,” Wiggins says. “[The Times] wanted to build out a data science function not only to curate and make available those data, but to learn from those data. In particular, the thing that the New York Times is interested in learning is: what makes for a good long-term relationship with a reader?”
“On every desk in the newsroom, reporters are starting to understand that if you don’t know how to understand and manipulate data, someone who can will be faster than you, “said Scott Klein, a managing editor at ProPublica.He continued: Can you imagine a sports reporter who doesn’t know what an on-base percentage is? Or doesn’t know how to calculate it himself? You can now ask a version of that question for almost every beat. There are more and more reporters who want to have their own data and to analyze it themselves. Take, for example, my colleague, Charlie Ornstein. In addition to being a Pulitzer Prize-winner, he’s one of the most sophisticated data reporters anywhere. He pores over new and insanely complex data sets himself. He has hit the edge of Access’ abilities and is switching to SQL Server. His being able to work and find stories inside data independently is hugely important for the work he does.
Read about this here:
Maybe it is time for the Analytics profession to wake up & bring some variety into their hiring-a journalist amongst their midst, maybe!
Analytics needs a evangelist! Without such a person, you just don’t get the impact that Analytics actually is capable of providing! Mostly this evangelist needs to be right at the top, the CEO!
Of course, some CMOs have led their organizations into embracing the practice, including John Costello, former exec VP-CMO of Home Depot; John Elkins, head of global brand and marketing at Visa International; and Cathy Lyons, CMO-exec VP at Hewlett-Packard.
One organization which has become a huge case study in the application of a “fact” based approach to business is Harrah’s Enetrtainment!
In 1998, as Harrah’s was about to embark on wave of expansion, their CEO Philip Satre asked Gary Loveman to take a break from Harvard to become chief operating officer of Harrah’s Entertainment. The important thing was the he was not brought in as a CMO but as the COO-he had the line authority to make changes that would impact the business!!
“In terms of income, it was actually a pay cut,” Loveman says, since he had to forego the consulting that supplemented his income as a professor.
He went on to develop the gaming industry’s most successful loyalty and analytics program—Total Rewards—which boasts more than 40 million members.
In an interesting article, Karl Taro Greenfeld says this about Gary Loveman, who has since then also become the CEO: the chief executive officer of Harrah’s Entertainment Inc., the largest gaming corporation in the world, sees his customers as a set of probabilities wrapped in human flesh.
Since taking over as CEO in 2003, Loveman, 50, has relied on the numbers to build Harrah’s from a regional operator of 15 casinos to one with 39 in the U.S. and 13 more overseas.
His first big move as COO was to start a loyalty program called Total Rewards, which became such a success -- growing to over 40 million members by 2010, the largest database of probabilities in the industry -- that by the time Satre stepped down in 2003, Loveman had become the logical choice to succeed him.
Loveman earned a Ph.D. in economics at MIT and went on to become CEO, president, and chairman of Caesars Entertainment, owner of Harrah's casinos and other resorts worldwide.
Loveman says there are three ways to get fired from the hotel and casino company: theft, sexual harassment, and running an experiment without a control group.
But this seems like common sense, run experiments , see what works & scale up! And yet very few companies do it.
Dan Ariely, a behavioral economics professor at Duke University and the author of Predictably Irrational, outlined some of the resistance to experimentation that he's come up against.
“I’ve often tried to help companies do experiments, and usually I fail spectacularly,” Ariely writes. For a company struggling with getting a good bonus system in place, he suggested experiments or even just a survey. Management, he says, “didn’t want to add to the trouble by messing with people’s bonuses merely for the sake of learning. But the employees are already unhappy, I thought, and the experiments would have provided evidence for how to make them less so in the years to come.”
But Gary Loveman managed to stay incredibly committed to Testing. These tests run from the use of coupons to offers of free meals or hotel stays, all designed to get customers to spend more money during their playtime.
This is what he said when asked about the Testing culture: “We need to overcome hunch and intuition with empirical evidence. . . . We can start with a hunch or strong belief, but we act on it through experiment. We want evidence. We’ve gone from the introduction of experimentation as a technique to a culture of experimentation as a business discipline. We hire people predisposed to do this by temperament and by background. Organizationally, we’re committed—and I’m committed—to making sure we have the discipline to have the decisions we make informed by this evidence”.
And yet we mustn’t forget that Harrah’s is not an easy business to run. Currently they have,$23 billion in long-term debt & have gone through some aggressive financial re structuring.
And lastly we must also ask ourselves, is this kind of Analytics good for society! Keeping gamblers coming back may hurt them & cause a lot of turmoil in many lives! Doesn't analytics have a social responsibility!
I saw this wonderful video of Vinod Khosla interviewing Larry Page & Sergey Brin.
4 year view or a 20 year view!!
It raises some very interesting questions. What do companies need to do to grow? How should companies look at the Short term vs long term? Taking a 4 year view vs a 20 year view are two fundamentally different philosophies. It is difficult to solve a “big problem” in 4 years & easy to do in 20 years. Google, of course likes to take on “big problems”.
So is Google a search company or will it be a larger Health company in the future. Or will it be an Artificial intelligence company?
Do have a look at this (long) but interesting video.
So Short term vs Long term? How many traditional companies would invest in something like Google Brain- a machine learning initiative to help make computing more efficient and capable by mimicking the distributed processes of the human brain. And yet Artificial intelligence is more than 60 years old as an application area. One reason why some experts believe AI is beginning to achieve its long-imagined potential is the explosion of data on the web.
So the question we need to ask is whether we have a 4 year view of Analytics or a 20 year view?
Maybe this may lead to the following questions to ponder over:
- Does your company do analytics or does it compete with analytics?
- Does "deep personalisation" have a role to play in your company & industry?
- Do analytics team participate in deeper strategic & longer term decisions in the company?
- Do Analytics folk with their deep specialist background have the skills to participate in such initiatives?
- Will unsupervised techniques like AI begin to threaten the Analytics profession (as we know it now); will it reduce the need for data scientists?
The best shopping list is one you don't actually have to create. When a giant like Walmart changes its mind about running a Loyalty program, it is time to sit & take notice.
Walmart has been synonymous with Everyday Low Prices(EDP). But unlike other supermarket chains like Kroger and Safeway, Walmart did not have a crucial marketing element-- A loyalty card.
Now Walmart is taking a distinctly different route towards loyalty. Savings Catcher, which began with a seven-city test this spring and rolls out nationally this summer, automatically gives shoppers refunds for the difference between what they paid at Walmart and lower prices advertised by competitors.
@WalmartLabs in Silicon Valley is planning to make shopper data and analytics from the program available to shoppers themselves, in a departure from most loyalty programs. Walmart is building capabilities that will let people search and sort their receipts, get pie charts breaking down how they spend their money, generate "predictive shopping lists," keep a running tab of in-store purchases to stay on budget, get notifications when there's a manufacturer coupon available for an item on their list, or get the best-priced bundle of items within a pre-set budget.
More importantly, this is an interesting trend where companies are beginning to use analytics for “consumer consumption”.